Analysis and forecasting of port logistics using TEI@I methodology

Xin TIAN, Liming LIU, K. K. LAI, Shouyang WANG

Research output: Journal PublicationsJournal Article (refereed)Researchpeer-review

7 Citations (Scopus)

Abstract

This paper presents an integrated forecasting model based on the TEI@I methodology for forecasting demand for port logistics services - specifically, port container throughput. The model analyzes port logistics time series data and other information in several steps. In the first step, several econometric models are built to forecast the linear segment of port logistics time series. In the second step, a radial basis function neural network is developed to predict the nonlinear segment of the time series. In the third step, the event-study method and expert system techniques are applied to evaluate the effects of economic and other events that may impact demand for port logistics. In the final step, synthetic forecasting results are obtained, based on the integration of predictions from the above three steps. For an illustration, Hong Kong port's container throughput series is used as a case study. The empirical results show the effectiveness of the TEI@I integrated model for port logistics forecasting.
Original languageEnglish
Pages (from-to)685-702
Number of pages18
JournalTransportation Planning and Technology
Volume36
Issue number8
Early online date30 Oct 2013
DOIs
Publication statusPublished - 2013

Fingerprint

Logistics
logistics
methodology
time series
Time series
Containers
Throughput
event
demand
knowledge-based system
neural network
Expert systems
study method
econometrics
Hong Kong
expert system
analysis
Neural networks
Economics
economics

Keywords

  • TEI@I methodology
  • artificial neural network
  • container throughput
  • econometric models
  • forecasting
  • port logistics

Cite this

TIAN, Xin ; LIU, Liming ; LAI, K. K. ; WANG, Shouyang. / Analysis and forecasting of port logistics using TEI@I methodology. In: Transportation Planning and Technology. 2013 ; Vol. 36, No. 8. pp. 685-702.
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Analysis and forecasting of port logistics using TEI@I methodology. / TIAN, Xin; LIU, Liming; LAI, K. K.; WANG, Shouyang.

In: Transportation Planning and Technology, Vol. 36, No. 8, 2013, p. 685-702.

Research output: Journal PublicationsJournal Article (refereed)Researchpeer-review

TY - JOUR

T1 - Analysis and forecasting of port logistics using TEI@I methodology

AU - TIAN, Xin

AU - LIU, Liming

AU - LAI, K. K.

AU - WANG, Shouyang

PY - 2013

Y1 - 2013

N2 - This paper presents an integrated forecasting model based on the TEI@I methodology for forecasting demand for port logistics services - specifically, port container throughput. The model analyzes port logistics time series data and other information in several steps. In the first step, several econometric models are built to forecast the linear segment of port logistics time series. In the second step, a radial basis function neural network is developed to predict the nonlinear segment of the time series. In the third step, the event-study method and expert system techniques are applied to evaluate the effects of economic and other events that may impact demand for port logistics. In the final step, synthetic forecasting results are obtained, based on the integration of predictions from the above three steps. For an illustration, Hong Kong port's container throughput series is used as a case study. The empirical results show the effectiveness of the TEI@I integrated model for port logistics forecasting.

AB - This paper presents an integrated forecasting model based on the TEI@I methodology for forecasting demand for port logistics services - specifically, port container throughput. The model analyzes port logistics time series data and other information in several steps. In the first step, several econometric models are built to forecast the linear segment of port logistics time series. In the second step, a radial basis function neural network is developed to predict the nonlinear segment of the time series. In the third step, the event-study method and expert system techniques are applied to evaluate the effects of economic and other events that may impact demand for port logistics. In the final step, synthetic forecasting results are obtained, based on the integration of predictions from the above three steps. For an illustration, Hong Kong port's container throughput series is used as a case study. The empirical results show the effectiveness of the TEI@I integrated model for port logistics forecasting.

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KW - artificial neural network

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KW - econometric models

KW - forecasting

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